Source code for lsst.sims.maf.slicers.baseSpatialSlicer

from builtins import zip
from builtins import range
# The base class for all spatial slicers.
# Slicers are 'data slicers' at heart; spatial slicers slice data by RA/Dec and
#  return the relevant indices in the simData to the metric.
# The primary things added here are the methods to slice the data (for any spatial slicer)
#  as this uses a KD-tree built on spatial (RA/Dec type) indexes.

import warnings
import numpy as np
from functools import wraps
from lsst.sims.maf.plots.spatialPlotters import BaseHistogram, BaseSkyMap

# For the footprint generation and conversion between galactic/equatorial coordinates.
from lsst.obs.lsst import LsstCamMapper
from lsst.sims.coordUtils import _chipNameFromRaDec
import lsst.sims.utils as simsUtils

from .baseSlicer import BaseSlicer

__all__ = ['BaseSpatialSlicer']


[docs]class BaseSpatialSlicer(BaseSlicer): """Base spatial slicer object, contains additional functionality for spatial slicing, including setting up and traversing a kdtree containing the simulated data points. Parameters ---------- lonCol : str, optional Name of the longitude (RA equivalent) column to use from the input data. Default fieldRA latCol : str, optional Name of the latitude (Dec equivalent) column to use from the input data. Default fieldDec latLonDeg : boolean, optional Flag indicating whether lat and lon values from input data are in degrees (True) or radians (False). Default True. verbose : boolean, optional Flag to indicate whether or not to write additional information to stdout during runtime. Default True. badval : float, optional Bad value flag, relevant for plotting. Default -666. leafsize : int, optional Leafsize value for kdtree. Default 100. radius : float, optional Radius for matching in the kdtree. Equivalent to the radius of the FOV. Degrees. Default 1.75. useCamera : boolean, optional Flag to indicate whether to use the LSST camera footprint or not. Default False. rotSkyPosColName : str, optional Name of the rotSkyPos column in the input data. Only used if useCamera is True. Describes the orientation of the camera orientation compared to the sky. Default rotSkyPos. mjdColName : str, optional Name of the exposure time column. Only used if useCamera is True. Default observationStartMJD. chipNames : array-like, optional List of chips to accept, if useCamera is True. This lets users turn 'on' only a subset of chips. Default 'all' - this uses all chips in the camera. scienceChips : bool (True) Do not include wavefront sensors when checking if a point landed on a chip. """ def __init__(self, lonCol='fieldRA', latCol='fieldDec', latLonDeg=True, verbose=True, badval=-666, leafsize=100, radius=1.75, useCamera=False, rotSkyPosColName='rotSkyPos', mjdColName='observationStartMJD', chipNames='all', scienceChips=True): super(BaseSpatialSlicer, self).__init__(verbose=verbose, badval=badval) self.lonCol = lonCol self.latCol = latCol self.latLonDeg = latLonDeg self.rotSkyPosColName = rotSkyPosColName self.mjdColName = mjdColName self.columnsNeeded = [lonCol, latCol] self.useCamera = useCamera if useCamera: self.columnsNeeded.append(rotSkyPosColName) self.columnsNeeded.append(mjdColName) self.slicer_init = {'lonCol': lonCol, 'latCol': latCol, 'radius': radius, 'badval': badval, 'useCamera': useCamera} self.radius = radius self.leafsize = leafsize self.useCamera = useCamera self.chipsToUse = chipNames self.scienceChips = scienceChips # RA and Dec are required slicePoint info for any spatial slicer. Slicepoint RA/Dec are in radians. self.slicePoints['sid'] = None self.slicePoints['ra'] = None self.slicePoints['dec'] = None self.nslice = None self.shape = None self.plotFuncs = [BaseHistogram, BaseSkyMap]
[docs] def setupSlicer(self, simData, maps=None): """Use simData[self.lonCol] and simData[self.latCol] (in radians) to set up KDTree. Parameters ----------- simData : numpy.recarray The simulated data, including the location of each pointing. maps : list of lsst.sims.maf.maps objects, optional List of maps (such as dust extinction) that will run to build up additional metadata at each slicePoint. This additional metadata is available to metrics via the slicePoint dictionary. Default None. """ if maps is not None: if self.cacheSize != 0 and len(maps) > 0: warnings.warn('Warning: Loading maps but cache on.' 'Should probably set useCache=False in slicer.') self._runMaps(maps) self._setRad(self.radius) if self.useCamera: self._setupLSSTCamera() self._presliceFootprint(simData) else: if self.latLonDeg: self._buildTree(np.radians(simData[self.lonCol]), np.radians(simData[self.latCol]), self.leafsize) else: self._buildTree(simData[self.lonCol], simData[self.latCol], self.leafsize) @wraps(self._sliceSimData) def _sliceSimData(islice): """Return indexes for relevant opsim data at slicepoint (slicepoint=lonCol/latCol value .. usually ra/dec).""" # Build dict for slicePoint info slicePoint = {} if self.useCamera: indices = self.sliceLookup[islice] slicePoint['chipNames'] = self.chipNames[islice] else: sx, sy, sz = simsUtils._xyz_from_ra_dec(self.slicePoints['ra'][islice], self.slicePoints['dec'][islice]) # Query against tree. indices = self.opsimtree.query_ball_point((sx, sy, sz), self.rad) # Loop through all the slicePoint keys. If the first dimension of slicepoint[key] has # the same shape as the slicer, assume it is information per slicepoint. # Otherwise, pass the whole slicePoint[key] information. Useful for stellar LF maps # where we want to pass only the relevant LF and the bins that go with it. for key in self.slicePoints: if len(np.shape(self.slicePoints[key])) == 0: keyShape = 0 else: keyShape = np.shape(self.slicePoints[key])[0] if (keyShape == self.nslice): slicePoint[key] = self.slicePoints[key][islice] else: slicePoint[key] = self.slicePoints[key] return {'idxs': indices, 'slicePoint': slicePoint} setattr(self, '_sliceSimData', _sliceSimData)
def _setupLSSTCamera(self): """If we want to include the camera chip gaps, etc""" mapper = LsstCamMapper() self.camera = mapper.camera self.epoch = 2000.0 def _presliceFootprint(self, simData): """Loop over each pointing and find which sky points are observed """ # Now to make a list of lists for looking up the relevant observations at each slicepoint self.sliceLookup = [[] for dummy in range(self.nslice)] self.chipNames = [[] for dummy in range(self.nslice)] # Make a kdtree for the _slicepoints_ # Using scipy 0.16 or later self._buildTree(self.slicePoints['ra'], self.slicePoints['dec'], leafsize=self.leafsize) # Loop over each unique pointing position if self.latLonDeg: lat = np.radians(simData[self.latCol]) lon = np.radians(simData[self.lonCol]) else: lat = simData[self.latCol] lon = simData[self.lonCol] for ind, ra, dec, rotSkyPos, mjd in zip(np.arange(simData.size), lon, lat, simData[self.rotSkyPosColName], simData[self.mjdColName]): dx, dy, dz = simsUtils._xyz_from_ra_dec(ra, dec) # Find healpixels inside the FoV hpIndices = np.array(self.opsimtree.query_ball_point((dx, dy, dz), self.rad)) if hpIndices.size > 0: obs_metadata = simsUtils.ObservationMetaData(pointingRA=np.degrees(ra), pointingDec=np.degrees(dec), rotSkyPos=np.degrees(rotSkyPos), mjd=mjd) chipNames = _chipNameFromRaDec(self.slicePoints['ra'][hpIndices], self.slicePoints['dec'][hpIndices], epoch=self.epoch, camera=self.camera, obs_metadata=obs_metadata) if self.scienceChips: # I think it's W for wavefront sensor good = np.flatnonzero(np.core.defchararray.find(chipNames.astype(str), 'W') == -1) chipNames = chipNames[good] hpIndices = hpIndices[good] # If we are using only a subset of chips if self.chipsToUse != 'all': checkedChipNames = [chipName in self.chipsToUse for chipName in chipNames] good = np.where(checkedChipNames)[0] chipNames = chipNames[good] hpIndices = hpIndices[good] # Find the healpixels that fell on a chip for this pointing good = np.where(chipNames != [None])[0] hpOnChip = hpIndices[good] for i, chipName in zip(hpOnChip, chipNames[good]): self.sliceLookup[i].append(ind) self.chipNames[i].append(chipName) if self.verbose: "Created lookup table after checking for chip gaps." def _buildTree(self, simDataRa, simDataDec, leafsize=100): """Build KD tree on simDataRA/Dec using utility function from mafUtils. simDataRA, simDataDec = RA and Dec values (in radians). leafsize = the number of Ra/Dec pointings in each leaf node.""" self.opsimtree = simsUtils._buildTree(simDataRa, simDataDec, leafsize) def _setRad(self, radius=1.75): """Set radius (in degrees) for kdtree search using utility function from mafUtils.""" self.rad = simsUtils.xyz_angular_radius(radius)